View source for Unawareness (to a sensitive attribute)

{{see also|Machine learning terms}}
==Unawareness in Machine Learning==
Unawareness in machine learning refers to the deliberate exclusion or ignorance of specific sensitive attributes during the process of model training and decision-making. Sensitive attributes are those that may potentially lead to unfair or discriminatory outcomes, such as race, gender, age, or sexual orientation. The primary goal of incorporating unawareness in machine learning is to ensure fairness and unbiased decision-making in the resulting models.

===Motivation===
The motivation behind incorporating unawareness in machine learning is to mitigate the potential for biased outcomes when algorithms process data containing sensitive attributes. By excluding these attributes, the model is less likely to produce discriminatory results. For instance, a hiring algorithm that doesn't consider an applicant's race would be less likely to exhibit racial bias in its recommendations. Unawareness helps ensure that models are more equitable, and that the benefits of machine learning technologies are distributed fairly across different groups in society.

===Challenges===
Despite the benefits of incorporating unawareness in machine learning, it comes with its own set of challenges. Some of these challenges include:

* '''Indirect discrimination''': Even if a model is unaware of a sensitive attribute, it may still exhibit bias if other non-sensitive attributes are correlated with the sensitive attribute. For example, a model that doesn't consider race might still discriminate based on zip code, which can be highly correlated with race.

* '''Loss of information''': By excluding sensitive attributes, models may lose valuable information that could be important for accurate predictions. This trade-off between fairness and accuracy is a significant challenge in achieving unbiased models.

* '''Context-specific decisions''': In some cases, being aware of sensitive attributes might be necessary to make fair decisions. For instance, in medical applications, understanding the patient's age, gender, or ethnicity could be crucial for accurate diagnosis and treatment.

==Explain Like I'm 5 (ELI5)==
Unawareness in machine learning is like a teacher who doesn't know about the students' favorite colors, so they can't pick teams based on color preferences. This way, the teacher can make sure that teams are formed fairly, without favoring any particular color. In machine learning, this means not using information like a person's race, gender, or age to make decisions. This helps the computer make fair choices for everyone. But sometimes, not knowing these things can also make it harder for the computer to do its job well, so it's important to find a balance between being fair and being accurate.


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